AI-Powered Personalized Credit Card Recommendations

Explain data warehouse architecture from source systems through staging, warehouse, lake, and mart to BI tools and ML models, using ETL and CDC for insurance case size prediction.

Explore the data source structure behind ai-powered personalized credit card recommendations, including the dim tables for applications, cards, customers, preferences, interactions, and approvals.

Create a data warehouse schema in MySQL Workbench, build seven dw tables, including dim customer and dim credit card, and prepare for ETL with Apache NiFi.

Load data from csv files into a MySQL data warehouse using Apache NiFi, building an ETL pipeline for dim branch, dim customers, and other dimensions.

Import libraries such as pandas, seaborn, and matplotlib, load data via SQLAlchemy, split into train and test sets, and use random forest models with informative visualizations.

Explore the random forest algorithm, an ensemble of decision trees that reduces overfitting via bootstrapping and random feature selection for classification and regression.

Learn feature engineering and training of a random forest to generate personalized credit card recommendations, with label encoding, train-test split, evaluation, and real-time predictions.

Save, load, and apply trained random forest models to predict approval probability, credit limits, and reward type preferences for new customers using properly preprocessed data.

Understand XGBoost, a fast, flexible gradient boosting algorithm for structured tabular data built from sequential decision trees that minimize loss by modeling residuals and updating predictions with a learning rate.

Leverage XGBoost to predict approval probability, encode the binary target, split data for training and testing, and optimize hyperparameters with grid search to improve accuracy.

Explore item-based collaborative filtering using cosine similarity to predict ratings and generate recommendations, including data collection, similarity matrices, and weighted predictions, with Python and scikit-learn.

Apply item-based collaborative filtering to recommend similar credit cards using cosine similarity, add-on products, and tailored bundles by analyzing customer interactions.

Extend item-based collaborative filtering with cosine similarity, weights for interactions, and time decay to deliver personalized product recommendations using enriched data and user preferences.

Install and run Apache NiFi on Windows, configure Java and environment variables, access the NiFi UI, and design your own ETL pipelines.

Install Anaconda and Python 3.8 by downloading from the official sites, following the installer wizard, and verifying the installation to prepare your environment for Python coding.